Statistics feature embedding for heart sound classification

Mohammad Adiban – Bagher BabaAli – Saeedreza Shehnepoor

   Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classification on the Physionet challenge 2016 dataset. For the first time, a fixed length feature vector; called i-vector; is extracted from each heart sound using Mel frequency cepstral coefficient (MFCC) features. Afterwards, principal component analysis (PCA) transform and variational autoencoder (VAE) are applied on the i-vector to achieve dimension reduction. Eventually, the reduced size vector is fed to Gaussian mixture models (GMMs) and support vector machine (SVM) for classification purpose. Experimental results demonstrate the proposed method could achieve a performance improvement of 16 % based on modified accuracy (MAcc) compared with the baseline system on the Physionet2016 dataset.

Keywords: heart sound classification, i-vector, Gaussian mixture models, support vector machine, principal component analysis, variational autoencoders

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